The USGS recently released high resolution elevation data as a lidar point cloud called USGS 3DEP in a public dataset on Amazon. This dataset is essential to build models of water flow and predict plant health and maize harvest.

The purpose of this package (usgslidar) is to produce an easy to use, reliable and well designed python module that domain experts and data scientists can use to fetch, visualise, and transform publicly available satellite and LIDAR data. In particular, it interfaces with USGS 3DEP and fetches data available in the s3 instance.

Table of content

1. Setup
2. Usage
3. Getting started (tutorial)
4. Example notebook

Setup

To build our pipeline we actually use PDAL under the hood. Since PDAL doesnt build properly with pip in the current version, you need to use conda to manage your dependencies.

• Install Anaconda
• In a conda environment install PDAL and python-pdal

  conda install -c conda-forge pdal python-pdal

• Install usgslidar

  pip install usgslidar

Usage

Full Documentation for usgslidar package can be found here.

Getting started

Lets view an example of visualizing a point cloud data on a farm in Iowa

• From the get_data module lets import the get_geopandas_dataframe module

  from usgslidar.get_data import get_geopandas_dataframe

• We will get back the elevation and geometry dataframe when we call the get_geopandas_dataframe function

  elevation_df = get_geopandas_dataframe()

• Lets now import the plot_3d_map function from the visualize module to help us plot the 3d map

  from usgslidar.visualize import plot_3d_map

• We can now plot the 3d map calling the function and passing in the elevation dataframe we got above

  plot_3d_map(df=elevation_df)

• We will get an output of a 3d plot like below that we can play around with

Examples

Example notebooks with specific purposes can be found here.